我创建了以下 pandas 数据框:
import numpy as np
import pandas as pd
ds = {'col1' : [11,22,33,24,15,6,7,68,79,10,161,12,113,147,115]}
df = pd.DataFrame(data=ds)
predFeature = []
for i in range(len(df)):
predFeature.append(0)
predFeature[i] = predFeature[i-1]+1
df['predFeature'] = predFeature
arrayTarget = []
arrayPred = []
target = np.array(df['col1'])
predFeature = np.array(df['predFeature'])
for i in range(len(df)):
arrayTarget.append(target[i-4:i])
arrayPred.append(predFeature[i-4:i])
df['arrayTarget'] = arrayTarget
df['arrayPred'] = arrayPred
看起来像这样:
col1 predFeature arrayTarget arrayPred
0 11 1 [] []
1 22 2 [] []
2 33 3 [] []
3 24 4 [] []
4 15 5 [11, 22, 33, 24] [1, 2, 3, 4]
5 6 6 [22, 33, 24, 15] [2, 3, 4, 5]
6 7 7 [33, 24, 15, 6] [3, 4, 5, 6]
7 68 8 [24, 15, 6, 7] [4, 5, 6, 7]
8 79 9 [15, 6, 7, 68] [5, 6, 7, 8]
9 10 10 [6, 7, 68, 79] [6, 7, 8, 9]
10 161 11 [7, 68, 79, 10] [7, 8, 9, 10]
11 12 12 [68, 79, 10, 161] [8, 9, 10, 11]
12 113 13 [79, 10, 161, 12] [9, 10, 11, 12]
13 147 14 [10, 161, 12, 113] [10, 11, 12, 13]
14 115 15 [161, 12, 113, 147] [11, 12, 13, 14]
我需要生成一个名为
slope
的新列,它对应于为每行训练的线性回归的系数,并且:
arrayTarget
arrayPred
例如:
前 4 行的
slope
是 null
。
第五行的斜率由线性回归系数给出,该系数考虑以下值:
[1, 2, 3, 4]
[11, 22, 33, 24]
结果将是:0.10204081632653061
。第 6 行的斜率由线性回归系数给出,该系数考虑以下值:
[2, 3, 4, 5]
[22, 33, 24, 15]
结果将是:-0.09090909090909091
。等等。
有人可以帮助我吗?
您可以定义一个使用
sklearn.linear_model.LinearRegression
的函数并将其应用于 axis=1
。如果您的数据框太大,效率不会很高。
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
def calculate_slope(x, y):
if len(x) < 1:
return np.nan
lr.fit(x.reshape(-1, 1), y)
return lr.coef_[0]
df["slope"] = df.apply(
lambda x: calculate_slope(x["arrayTarget"], x["arrayPred"]), axis=1
)
col1 predFeature arrayTarget arrayPred slope
0 11 1 [] [] NaN
1 22 2 [] [] NaN
2 33 3 [] [] NaN
3 24 4 [] [] NaN
4 15 5 [11, 22, 33, 24] [1, 2, 3, 4] 0.102041
5 6 6 [22, 33, 24, 15] [2, 3, 4, 5] -0.090909
6 7 7 [33, 24, 15, 6] [3, 4, 5, 6] -0.111111
7 68 8 [24, 15, 6, 7] [4, 5, 6, 7] -0.142857
8 79 9 [15, 6, 7, 68] [5, 6, 7, 8] 0.030418
9 10 10 [6, 7, 68, 79] [6, 7, 8, 9] 0.030769
10 161 11 [7, 68, 79, 10] [7, 8, 9, 10] 0.002331
11 12 12 [68, 79, 10, 161] [8, 9, 10, 11] 0.009048
12 113 13 [79, 10, 161, 12] [9, 10, 11, 12] -0.001640
13 147 14 [10, 161, 12, 113] [10, 11, 12, 13] 0.004698
14 115 15 [161, 12, 113, 147] [11, 12, 13, 14] 0.002174